From theory to practice - page 1550
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I forget exactly what it's called.
I see a lot of people here, please tell me the formula for the rate of return to zero.
I forget exactly what it's called.
There is no such general formulae. Maybe only in particular examples. The rate of return to the origin of motion in random walks is the root of time, the angular velocity of a pendulum is another way. Depends on what problem you are solving.
The funny thing is that in tests I have crazy profits, great deals. So, once or twice a month there are some powerful trends that crush my TS, who doesn't? But, all in all - good.
As soon as I go real, I get this trend... It's a hell of a thing...
Jesus, am I dumb as a doorknob or what?! Help me! Amen.
So, have you applied it yet? At least write something in the PM about the results.
So have the conversions been applied yet? At least post something in the PM about the research results.
This is at Max's discretion. If it were not for his initiative, there would be no research. But, there is nothing unusual there yet - there is still a long way to go.
There is no such general formulae. Maybe only in particular examples. The rate of return to the origin of motion in random walks is the root of time, the angular velocity of a pendulum is another way. Depends on what problem you are solving.
Yes there is such, even it has a name, as long ago as that I met, but I have forgotten as called.
I want to know how long it takes for the stationary series to return to zero.
Yes there is such, even it has a name, as long ago as that I met but forgot as called.
That is, I want to know in what time, the stationary series returns to zero.
Well, that's what I'm saying - average time to return to the starting point (to conditional zero) = y^2/D, where y is the coordinate of the point making the random walk, D is the variance.
Note that we are talking about average time, no one will ever say exactly.
Yes there is such, even it has a name, I met it long ago but forgot how it is called.
That is, I want to know how long it takes for the stationary series to return to zero.
How about Poincaré's return theorem? Stationarity is not enough there - ergodicity is needed.
There are also statements about unit probability of reaching any point for one- and two-dimensional SB, but these are not stationary processes (the variance grows with time).
Well, that's what I'm saying - average time to return to the starting point (to conditional zero) = y^2/D, where y is the coordinate of the point making the random walk, D is the variance.
Note that we are talking about average time, no one will ever say exactly.
Thank you, that's what I couldn't formulate "return time to the starting point".
The formula might be what is needed, sadly only the average, but at least now there is a starting point where to dig, maybe there is an exact definition.
I take it we're talking about options, where you have to calculate the timing of the trade? Yes, it's a funny thing. I have no research at all on this topic, most likely - there really are stationary Gaussian processes, but...
Once again - in all scenarios we can only talk about the average time with a certain standard error.
I take it we're talking about options, where you have to calculate the timing of the trade? Yes, it's a funny thing. I have no research at all on this topic, most likely - there really are stationary Gaussian processes, but...
Once again - in all scenarios we can only talk about the average time with a certain standard error.
The option decay time is calculated using the Greeks, although it depends on which type of analysis is used, perhaps stationarity can be applied there as well, I don't know about that.
In fact, you can count anywhere stationarity is observed.